Description:
Information technology and high-tech systems used daily in hospitals increase the demand for measuring information security threats in the healthcare industry. The impact of complexity of using new healthcare information and communications technology (ICT) devices places more pressure on the healthcare community to perceive different information systems (IS) security threats as they involve different IS management models. This research study focused on measuring the healthcare community perceptions and on the following issues to include definition of information security perception of information security threats perception of information security management and perception of information security in the context of another healthcare community. To assess to plan, design and implement a national information security strategy for their healthcare sector. A survey was developed and interviewed 60 healthcare employees from three hospitals based in Dubai. The research results assist in creating awareness for easy access to hands-on guidelines for the healthcare community, thereby increasing the awareness levels of information security in the United Arab Emirates (UAE). These results assist the UAE government to further adapt the training and education programs for the healthcare community to increase their effectiveness and efficiency levels of IT and subsequent IS development. A discussion of the current status of Dubai hospitals privacy, confidentiality and security challenge is presented.
Description:
Gene Regulatory Networks (GRNs) offer enhanced insight into the biological functions and biochemical pathways of cells associated with gene regulatory mechanisms. However, obtaining accurate GRNs that explain gene expressions and functional associations remains a difficult task. Only a few studies have incorporated heuristics into a GRN discovery process. Doing so has the potential to improve accuracy and reduce the search space and computational time. A technique for GRN discovery that integrates heuristic information into the discovery process is advanced. The approach incorporates three elements: 1) a novel 2D visualized coexpression function that measures the association between genes; 2) a post-processing step that improves detection of up, down and self-regulation and 3) the application of heuristics to generate a Hub network as the backbone of the GRN. Using available microarray and next generation sequencing data from Escherichia coli, six synthetic benchmark GRN datasets were generated with the neighborhood addition and cluster addition methods available in SynTReN. Results of the novel 2D-visualization co-expression function were compared with results obtained using Pearson's correlation and mutual information. The performance of the biological genetics-based heuristics consisting of the 2D-Visualized Co-expression function, post-processing and Hub network was then evaluated by comparing the performance to the GRNs obtained by ARACNe and CLR. The 2D-Visualized Co-expression function significantly improved gene-gene association matching compared to Pearson's correlation coefficient (t = 3.46, df = 5, p = 0.02) and Mutual Information (t = 4.42, df = 5, p = 0.007). The heuristics model gave a 60% improvement against ARACNe (p = 0.02) and CLR (p = 0.019). Analysis of Escherichia coli data suggests that the GRN discovery technique proposed is capable of identifying significant transcriptional regulatory interactions and the corresponding regulatory networks.
Description:
Set-level problems are as important as instance-level problems. The core in solving set-level problems is: how to measure the similarity between two sets. This paper investigates data-dependent kernels that are derived directly from data. We introduce Isolation Set Kernel which is solely dependent on data distribution, requiring neither class information nor explicit learning. In contrast, most current set-similarities are not dependent on the underlying data distribution. We theoretically analyze the characteristic of Isolation Set-Kernel. As the set-kernel has a finite feature map, we show that it can be used to speed up the set-kernel computation significantly. We apply Isolation Set-Kernel to Multi-Instance Learning (MIL) using SVM classifier, and demonstrate that it outperforms other set-kernels or other solutions to the MIL problem.
Description:
Intelligent Electronic Devices (IEDs) diagnose faults and traffic issues of data packets in a reliable and efficient manner in a substation environment. These devices determine abnormal system condition, wide area disturbances offering greater flexibility to the operator in terms of condition monitoring and diagnostics. In a distribution system, end user get multiple faults in the network and one such nuisance disruption of power on a faulty feeder results in sympathetic tripping. Sympathetic tripping occurs during high load conditions causing entire protection system to spuriously trip due to an out of section fault. Occurrence of such faults could be attributed to load unbalance in the distribution feeders, delayed voltage recovery conditions, voltage swell/sag in the feeders etc. Usually, this phenomenon occur when loads containing low inertia rotating equipment are connected to distribution feeders. In this paper, an Optimized Network Engineering Tool (OPNET) is used to simulate, mitigate faults attributed to Sympathetic Tripping in a laboratory environment.
Description:
The modern wireless technology exploiting the full potential of 5G IoT is the future for healthcare sector. In the healthcare sector, the 5G technology will maximize the performance and will reduce the chances of damage to the patient by providing careful and advance activity monitoring scenarios. We have proposed the idea of monitoring different body posture in Huntington disease by exploiting the low cost wireless devices operating at 4.8 GHz frequency. The system captures the wireless channel information for three body motions and classification of these motions was performed by using support vector machine. The SVM used 10 time-domain features for the classification process by using three main kernel functions, such as, Linear, Polynomial and Radial basis function. The system minimizes all the external noise by using the microwave absorbing materials. This increases the performance and feasibility of sensing body motions.
Description:
Student motivations are affected by many factors that are classified as intrinsic or/and extrinsic. This will influence students' engagement and consequently impacting their performance. To build a better judgment on the factors that affect student motivation, a comprehensive study should be applied in order to identify all factors and their drivers and how they influence student's motivation. Some of these factors have negative impact on motivation, while other have positive impact. In this paper, a multifactor based motivation has been assessed to distinguish the positive from negative factors. Then controlling student's motivation to enhance student engagement can be done through controlling these factors. The negative factors would be minimized or eliminated, and the positive factors will be encouraged and improved. Teaching institutions, universities and teachers play essential role in controlling these factors toward better motivation. A recommendation has been given to control the motivation factors that would lead to better student's engagement and performance.
Description:
The Internet of Things (IoT) has significantly increased the number of devices connected to the Internet ranging from sensors to multi-source data information. As the IoT continues to evolve with new technologies number of threats and attacks against IoT devices are on the increase. Analyzing and detecting these attacks originating from different sources needs machine learning models. These models provide proactive solutions for detecting attacks and their sources. In this paper, we propose to apply a supervised machine learning classification technique to identify cyber-attacks from each source. More precisely, we apply the incremental piecewise linear classifier that constructs boundary between sources/classes incrementally starting with one hyperplane and adding more hyperplanes at each iteration. The algorithm terminates when no further significant improvement of the separation of sources/classes is possible. The construction and usage of piecewise linear boundaries allows us to avoid any possible overfitting. We apply the incremental piecewise linear classifier on the multi-source real world cyber security data set to identify cyber-attacks and their sources.
Description:
Proceedings of the IEEE International Conference on Industrial Technology
Description:
The Internet of Things (IoT) has significantly increased the number of devices connected to the Internet ranging from sensors to multi-source data information. As the IoT continues to evolve with new technologies number of threats and attacks against IoT devices are on the increase. Analyzing and detecting these attacks originating from different sources needs machine learning models. These models provide proactive solutions for detecting attacks and their sources. In this paper, we propose to apply a supervised machine learning classification technique to identify cyber-attacks from each source. More precisely, we apply the incremental piecewise linear classifier that constructs boundary between sources/classes incrementally starting with one hyperplane and adding more hyperplanes at each iteration. The algorithm terminates when no further significant improvement of the separation of sources/classes is possible. The construction and usage of piecewise linear boundaries allows us to avoid any possible overfitting. We apply the incremental piecewise linear classifier on the multi-source real world cyber security data set to identify cyber-attacks and their sources.
Description:
Proceedings of the IEEE International Conference on Industrial Technology
Description:
This paper discusses nano-structured GaAs solar cell design and analysis for conversion efficiency improvement by increasing the light transmission and absorption, reducing the light reflection. The focus of this research is to construct different type of nano-grating shaped GaAs solar cells with various nano-grating heights and pitches, to compare the simulation results and find a suitable nano-structure that can provide higher conversion efficiency. Finite difference time domain (FDTD) simulation tool is used to simulate and calculate the light reflection, transmission and absorption for different nano-grating shapes, such as, parabolic, triangular, trapezoidal and rectangular. Based on the simulation results, it has been confirmed that the light reflection of parabolic shaped nano-grating structures is higher than triangular nano-grating structures, but it is lower than rectangular and trapezoidal shaped nano-grating structures. Moreover, the simulation results confirmed that the light transmission of parabolic shaped nano-grating structures is about 62.3% having a 200-nm grating height and an 810-nm grating pitch, which is about 22% higher than the rectangular (i.e., flat) type substrates.
Description:
This paper investigates the effect of different types of nano-grating structures embossed on top of the substrate of solar photovoltaic (PV) cell for high conversion efficiency. The simulation results for light reflection are obtained by using Opti-wave finite difference time-domain (Opti-FDTD) software. These nano-grating structures have different shapes, such as triangular, trapezoidal, pillar and parabolic. These nano-grating profiles work as a multilayer anti-reflective coating for GaAs solar cells and reduce the light reflection from the surface of the panel and increase the light trapping capacity inside the solar cell. These structures allow the gradual change in refractive index and provide a high transmission and less reflection of light that confirms excellent anti-reflective coating and increased light trapping capacity inside the cell substrate. For this simulation, different periodic shaped arrangements were made to obtain the higher conversion efficiency, the factors considered while develop the design are the aspect ratio (AR), thickness of the nano-grating structure and duty cycles. The simulation result shows that the light reflection loss in pillar shaped nano-grating structures having 150 nm of height and a 50% period (i.e., duty cycle) is ~0.5% only, which is the lowest reflection loss obtained, when compared with the triangular and trapezoidal shaped nano-grating structures, it is approximately 38% more efficient in trapping the incident light.
Description:
This research is supported by the School of Engineering and Technology, Melbourne, Victoria; Centre for Intelligent Systems, Brisbane, QLD, Central Queensland University, Australia.
Description:
This paper re-examines existing systems in streaming classification with emerging new classes (SENC) problems, where new classes that have not been used to train a classifier may emerge in a data stream. We identify that existing systems have an unspecified assumption that emerging new classes are geometrically far from known classes, or instances of known classes are densely distributed, in the feature space. Using a class separation indicator alpha, we refine the SENC problem into an alpha-SENC problem, where alpha indicates a geometric distance between two classes in the feature space. We show that while most existing systems work well in high-alpha SENC problems (i.e., a new class is geometrically far from a known class or instances of known classes are densely distributed), they perform poorly in low-alpha SENC problems. To solve low-alpha SENC problems effectively, we propose an approach using nearest neighbor ensembles or SENNE. We demonstrate that SENNE is able to handle both the low-alpha and high-alpha SENC problems which can appear at different times in a single data stream.
Description:
Electronic Health Records (EHRs) constitute evidence of online health information management. Critical healthcare information technology (HIT) infrastructure facilitates health information exchange of 'modern' health systems. The growth and implementation of EHRs are progressing in many countries while the adoption rate is lagging and lacking momentum amidst privacy and security concerns. This paper uses an interrupted time series (ITS) analysis of OECD data related to EHRs from many countries to make predictions about EHR adoption. The ITS model can be used to explore the impact of various HIT on adoption. Assumptions about the impact of Information Accountability are entered into the model to generate projections if information accountability technologies are developed. In this way, the OECD data and ITS analysis can be used to perform simulations for improving EHR adoption.
Description:
Electronic Health Records (EHRs) constitute evidence of online health information management. Critical healthcare information technology (HIT) infrastructure facilitates health information exchange of 'modern' health systems. The growth and implementation of EHRs are progressing in many countries while the adoption rate is lagging and lacking momentum amidst privacy and security concerns. This paper uses an interrupted time series (ITS) analysis of OECD data related to EHRs from many countries to make predictions about EHR adoption. The ITS model can be used to explore the impact of various HIT on adoption. Assumptions about the impact of Information Accountability are entered into the model to generate projections if information accountability technologies are developed. In this way, the OECD data and ITS analysis can be used to perform simulations for improving EHR adoption.
Description:
Real-world time series classification applications often involve positive unlabeled (PU) training data, where there are only a small set PL of positive labeled examples and a large set U of unlabeled ones. Most existing time series PU classification methods utilize all readings in the time series, making them sensitive to non-characteristic readings. Characteristic patterns named shapelets present a promising solution to this problem, yet discovering shapelets under PU settings is not easy. In this paper, we take on the challenging task of shapelet discovery with PU data. We propose a novel pattern ensemble technique utilizing both characteristic and non-characteristic patterns to rank U examples by their possibilities of being positive. We also present a novel stopping criterion to estimate the number of positive examples in U. These enable us to effectively label all U training examples and conduct supervised shapelet discovery. The shapelets are then used to build a one-nearest-neighbor classifier for online classification. Extensive experiments demonstrate the effectiveness of our method.
Description:
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Description:
Real-world time series classification applications often involve positive unlabeled (PU) training data, where there are only a small set PL of positive labeled examples and a large set U of unlabeled ones. Most existing time series PU classification methods utilize all readings in the time series, making them sensitive to non-characteristic readings. Characteristic patterns named shapelets present a promising solution to this problem, yet discovering shapelets under PU settings is not easy. In this paper, we take on the challenging task of shapelet discovery with PU data. We propose a novel pattern ensemble technique utilizing both characteristic and non-characteristic patterns to rank U examples by their possibilities of being positive. We also present a novel stopping criterion to estimate the number of positive examples in U. These enable us to effectively label all U training examples and conduct supervised shapelet discovery. The shapelets are then used to build a one-nearest-neighbor classifier for online classification. Extensive experiments demonstrate the effectiveness of our method.
Description:
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Description:
Remote asset management are faced with additional challenges in monitoring conditions, coordinating logistics for maintenance crew, transport and spare parts for maintenance delivery and asset replacements. Recent trends in technologies, remote performance monitoring and risk-based decision making in Capital Expenditure (CAPEX) and Operations and Maintenance Expenditure (OPEX) decisions for asset management are being embraced by asset intensive industries around the world, where critical assets are located in geographically distributed remote areas or difficult to inspect and maintain locations. Industries are also pushing boundaries by reducing crew size, deferring capital expenditure and overhauling and decision making in inspection and in some cases relaxing Original Equipment Manufacturers (OEM) recommended maintenance schedules. This paper discusses some of the issues and challenges with remote asset management. Illustrative example from heavy haul rail is used to explain reduction in Life Cycle Costs (LCC) and further enhancing operational performance.
Description:
Remote asset management are faced with additional challenges in monitoring conditions, coordinating logistics for maintenance crew, transport and spare parts for maintenance delivery and asset replacements. Recent trends in technologies, remote performance monitoring and risk-based decision making in Capital Expenditure (CAPEX) and Operations and Maintenance Expenditure (OPEX) decisions for asset management are being embraced by asset intensive industries around the world, where critical assets are located in geographically distributed remote areas or difficult to inspect and maintain locations. Industries are also pushing boundaries by reducing crew size, deferring capital expenditure and overhauling and decision making in inspection and in some cases relaxing Original Equipment Manufacturers (OEM) recommended maintenance schedules. This paper discusses some of the issues and challenges with remote asset management. Illustrative example from heavy haul rail is used to explain reduction in Life Cycle Costs (LCC) and further enhancing operational performance.
Description:
Recently, more and more attention is paid to reversible data hiding (RDH) in encrypted images because of its better protection of privacy compared with traditional RDH methods directly operated in original images. In several RDH algorithms, prediction-error expansion (PEE) is proved to be superior to other methods in terms of embedding capacity and distortion of marked image and multiple histograms modification (MHM) can realize adaptive selection of expansion bins which depends on image content in the modification of a sequence of histograms. Therefore, in this paper, we propose an efficient RDH method in encrypted images by combining PEE and MHM, and design corresponding mode of image partition. We first divide the image into three parts: W (for embedding secret data), B (for embedding the least significant bit(LSB) of W) and G (for generating prediction-error histograms). Then, we apply PEE and MHM to embed the LSB of W to reserve space for secret data. Next, we encrypt the image and change the LSB of W to realize the embedding of secret data. In the process of extraction, the reversibility of image and secret data can be guaranteed. The utilization of correlation between neighbor pixels and embedded order decided by the smoothness of pixel in part W contribute to the performance of our method. Compared to the existing algorithms, experimental results show that the proposed method can reduce distortion to the image at given embedding capacity especially at low embedding capacity.